The authors have nothing to disclose.

1. Preparing the Growth Medium
NOTE: The chemical composition of minimal medium M63 is as follows: 62 mM K2HPO4, 39 mM KH2PO4, 15 mM (NH4)2SO4, 1.8 &#181;M FeSO4, 15 &#181;M thiamine-HCl, 0.2 mM MgSO4, and 22 mM glucose. M63 is made by mixing three stock solutions: Five X solution, 20% glucose and MgSO4 thiamine solution. Store all solutions at 4 &#176;C.
<ol>
	<li>Preparing the Five X solution
	<ol>
		<li>To prepare FeSO4 solution, use an electrical pipette and a disposable serological pipette to add ddH2O to a 50-mL centrifuge tube. Use a P-200 to add 0.06 mL HCl to obtain 0.01 M HCl. Add 36 mg FeSO4-7H2O and mix well.</li>
		<li>Measure 160 mL ddH2O in a measuring cylinder and add it to a 500-mL beaker. Add the following in this order: 10.72 g K2HPO4, 5.24 g KH2PO4, 2.0 g (NH4)2SO4, and 0.5 mL FeSO4 solution (prepared in step 1.1.1).</li>
		<li>Using a precision pH meter, adjust the pH to 7.0 by adding 2 M KOH. Pour the solution in a measuring cylinder and add ddH2O to a total volume of 200 mL. Sterilize the solution using a 250-mL filtration unit (PVDF, 0.22 &#181;M).</li>
	</ol>
	</li>
	<li>Preparing the 20% glucose solution
	<ol>
		<li>Measure 200 mL ddH2O in a measuring cylinder and pour it to a 500-mL beaker. While mixing using a magnetic stirrer, add 50 g of glucose. Dilute the solution in a measuring cylinder to a total volume of 250 mL. Sterilize the solution as described in step 1.1.3.</li>
	</ol>
	</li>
	<li>Preparing the MgSO4 thiamine solution
	<ol>
		<li>Add 150 mL ddH2O to a 500-mL beaker. While mixing with a magnetic stirrer, add 1.0 g of thiamine-HCl and 10 g of MgSO4-7H2O. Dilute the solution in a measuring cylinder to a total volume of 200 mL. Sterilize the solution as described in step 1.1.3.</li>
	</ol>
	</li>
	<li>Preparing minimal medium M63
	<ol>
		<li>Place the Five X solution, the 20% glucose solution, the MgSO4 thiamine solution, an electronic pipette, a P-200 pipette, and 200-&#181;L pipette tips on a clean bench.</li>
		<li>Measure 155.8 mL ddH2O in a measuring cylinder and pour it into a 500-mL beaker.</li>
		<li>Using the electronic pipette and disposable serological pipette, add 40 mL of Five X solution and 4 mL 20% glucose solution to the measured ddH2O. Then, add 0.2 mL MgSO4 thiamine solution with the P-200. Sterilize the solution as described in step 1.1.3.</li>
	</ol>
	</li>
</ol>
2. Preparing the Glycerol Stock
<ol>
	<li>Cell culture 
	NOTE: The bacterial strains (e.g., W3110 and its reduced genomes) are available from strain bank organizations. The strains are usually obtained in the form of colonies on agar plates.
	<ol>
		<li>Place five sterilized glass tubes with silicone rubber stoppers, an electronic pipette, P-1,000, 1,000-&#181;L pipette tips, P-200, 200-&#181;L pipette tips, and the target strains (colonies on plates) on a clean bench.</li>
		<li>Expose the mouth of the glass tube to a Bunsen burner before opening the silicone rubber stopper. Expose the silicone rubber stopper to the flame after the tube is opened, and then lightly place the cap back on the glass tube.</li>
		<li>Use the electronic pipette and disposable serological pipette to add 5 mL M63 to one of the glass tubes and 4.5 mL M63 to the other four tubes.</li>
		<li>Use the P-200 tip to pick a colony and inoculate it in the glass tube containing 5 mL M63.</li>
		<li>Vortex the tube to make a suspension. Then, dilute the solution 10-fold by transferring 0.5 mL of this solution to one of the four tubes containing 4.5 mL M63.</li>
		<li>Repeat the process described in step 2.1.5 for the remaining tubes. A dilution series with five different concentrations (dilutions of 1, 10, 100, 1000, and 10,000) is now ready.</li>
		<li>Sterilize the mouths of the glass tubes and the silicone rubber stoppers as described in step 2.1.2. Cap the tubes with the stoppers. Avoid contamination by not wrinkling the silicone rubber stoppers.</li>
		<li>Place the five tubes in a pre-warmed shaking incubator at 37 &#176;C and shake at 200 rpm. Incubate the culture overnight or for 10 to 30 h.</li>
	</ol>
	</li>
	<li>Selection of the culture for the glycerol stock
	<ol>
		<li>Place the pre-warmed room temperature M63 medium, P-1,000, 1,000-&#181;L pipette tips, and a disposable cuvette on a clean bench.</li>
		<li>Add 1000 &#181;L M63 to a disposable cuvette with a P-1,000. Place the disposable cuvette in a spectrophotometer, start the program at a fixed wavelength of 600 nm, and measure the blank.</li>
		<li>Move the five glass tubes from the shaking incubator to the clean bench.</li>
		<li>Discard M63 from the disposable cuvette and add 1,000 &#181;L culture to the same disposable cuvette with a P-1,000. Measure the optical turbidity of the cell culture (OD600) as described in step 2.2.2. 
		NOTE: To avoid any contamination and to achieve a precise measurement, expose the glass tubes and the stoppers to the flame as described and vortex the culture before sampling. To ensure reliable results, repeated measurements are recommended, particularly when the cell density is low.</li>
		<li>Of the five cell cultures, choose one that is in the early exponential growth phase (OD600= 0.01 - 0.05) for the glycerol stock. 
		NOTE: If multiple cultures have densities within the optimal range, the one closest to 0.05 is commonly selected.</li>
	</ol>
	</li>
	<li>Make the glycerol stocks for repeated tests. 
	NOTE: This is described for preparing ten stocks. Larger or smaller amounts can be made according to the experimental requirements.
	<ol>
		<li>Place the sterilized 60% glycerol solution, ten sterilized 1.5-mL microtubes, P-1,000 and P-200, 1,000- and 200-&#181;L pipette tips, and a microtube stand on a clean bench.</li>
		<li>Add 250 &#181;L sterilized 60% glycerol solution and 750 &#181;L of the selected cell culture to the 1.5-mL microtube and mix by pipetting. 
		NOTE: The stock volume is variable, but always maintain a 1:3 ratio of 60% glycerol to cell culture; this results in a final concentration of 15% glycerol.</li>
		<li>Place the remaining nine microtubes in the microtube stand and dispense 100 &#181;L of the mixture prepared in step 2.3.2 to each tube. There are now ten identical glycerol stocks for future use.</li>
		<li>Store the stocks in a deep freezer at -80 &#176;C.</li>
	</ol>
	</li>
</ol>
3. Acquiring the Growth Curves
<ol>
	<li>Setting up the microplate reader. 
	NOTE: The terms shown in quotation marks show the specific phrasing used on the plate reader used here (see the Table of Materials).
	<ol>
		<li>Open the software. Open &#34;Protocols&#34; in &#34;Task Manager&#34; and choose &#34;Create New&#34;. Choose &#34;Standard Protocol&#34;.</li>
		<li>Open &#34;Procedure&#34;, and adjust the settings. Open &#34;Set Temperature&#34;, and select &#34;Incubator On&#34;. Set &#34;Temperature&#34; to 37 &#176;C and &#34;Gradient&#34; to 0 &#176;C. Check &#34;Preheat&#34; before continuing with the next step. Open &#34;Start Kinetic&#34;, set &#34;Run time&#34; for 24:00:00 or 48:00:00, and &#34;Interval&#34; for 00:30:00 or 01:00:00. 
		NOTE: It takes approximately 1 min to read an entire 96 well plate.</li>
		<li>Open &#34;Shake&#34; and set &#34;Shake Mode&#34; as &#34;Linear&#34;. Check &#34;Constitution Shake&#34; and set &#34;Frequency&#34; at 567 cpm. Open &#34;Read&#34;, check &#34;Absorbance&#34;, &#34;Endpoint/Kinetic&#34;, and &#34;Monochromators.&#34; Set &#34;Wavelength&#34; to 600.</li>
		<li>Click &#34;Validate&#34; to confirm that the procedure is correct. Click &#34;Save&#34; to save it as a new program for future use.</li>
	</ol>
	</li>
	<li>Real-time recording of growth
	<ol>
		<li>Draw a 96-well plate pictogram (8 &#215; 12 table) to indicate the positions of the inoculated culture samples on the 96-well plate. Print out the table and use it as a reference for the experiment. 
		NOTE: The wells located on the edges of the microplate should only contain blank medium because of evaporation.</li>
		<li>Place a sterilized 96-well flat bottom microplate with lid, P-1000, 1000-&#181;L pipette tips, P-200, 200-&#181;L pipette tips, several 1.5-mL microtubes, an 8-multichannel pipette, a sterilized reagent reservoir, room temperature M63, and the glycerol stocks (prepared in step 2.3) on a clean bench.</li>
		<li>Add approximately 25 mL M63 to the reagent reservoir. Use this reservoir stock for all the following steps.</li>
		<li>Add 900 &#181;L M63 to the microtubes in preparation for making serial dilutions.</li>
		<li>Thaw the glycerol stock at room temperature. Add 900 &#181;L M63 to the thawed glycerol stock and vortex. This results in a 10-fold dilution of the original glycerol stock.</li>
		<li>Transfer 100 &#181;L of the 10-fold dilution to another microtube containing 900 &#181;L of M63 and vortex. This results in a 100-fold dilution.</li>
		<li>Repeat step 3.2.6 until the desired number of dilutions are achieved.</li>
		<li>Fill the wells at the edge of the microplate with 200 &#181;L M63 using an 8-channel pipette (P-200). 
		NOTE: These wells can be used as the blank.</li>
		<li>Load 200 &#181;L of each diluted sample prepared in steps 3.2.4 - 3.2.7 to the microplate wells according to the reference table (step 3.2.1). Vortex the diluted samples prior to loading and load the same sample in multiple wells at varied locations on the plate.</li>
		<li>Place the 96-well microplate on the plate reader.</li>
		<li>Open &#34;Read Now&#34; in &#34;Task Manager&#34; and choose the program (section 3.1). Click &#34;OK&#34; to start measuring. Save the recording as a new experimental file for data analysis (section 4).</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>Export the results of the real-time growth-rate data (section 3.2) to a USB memory stick as a text file. 
	NOTE: A 96-well formatted results (curves) will display on the plate reader in real-time. The hourly records (OD values) can be exported as a table; the rows and columns represent the well numbers (e.g., A1, B1) and the measuring time (e.g., 00:00:59), respectively.</li>
	<li>Open the text file with a spreadsheet software.</li>
	<li>Copy the hourly OD600 reads of the 96-well microplate to a new worksheet for further analysis.</li>
	<li>Subtract the background reads at time zero from the hourly reads of each sample well. 
	NOTE: For convenience, the mean value of the blank wells containing M63 (step 3.2.8) can be used as the background value.</li>
	<li>Calculate the mean of five consecutive OD600 reads to estimate the maximal population density. 
	NOTE: The largest mean value of the five consecutive OD600 reads is defined as the maximal OD600 of the corresponding growth curve.</li>
	<li>Calculate the growth rate, &#181; (h-1), by applying the following equation for all pairs of consecutive values of OD600: 
	<img alt="Equation" src="/files/ftp_upload/56197/56197eq1.jpg" /> 
	NOTE: In this equation, Ci and Ci+1 represent the OD600 values of any two consecutive time points (ti and ti+1, respectively). LN indicates natural logarithm.</li>
	<li>Calculate the changes in the growth rate over time based on the hourly OD600 reads according to step 4.6. Calculate the mean and the standard deviation of five consecutive growth rates to estimate the maximal growth rate. 
	NOTE: The largest mean with the smallest standard deviation is defined as the maximal growth rate of the corresponding growth curve.</li>
</ol>
5. Confirming the Global Bias of the 96-well Reads (Optional)
NOTE: Both the plate reader and the consumable 96-well plate can cause biased measurements. To achieve highly precise and reproducible quantitative results, confirming the global bias of the 96-well plate is highly recommended.
<ol>
	<li>Prepare the 96-well plate and the plate reader for the bias test as described in section 3.2.2.</li>
	<li>Add 20 mL M63 to a sterilized 50-mL centrifuge tube. Add the glycerol stock to the same centrifuge tube and vortex. Transfer the suspended solution to a sterilized reagent reservoir.</li>
	<li>Use an 8-channel pipette to transfer 200 &#181;L of the suspended solution to the 96-well plate.</li>
	<li>Place the 96-well plate on the plate reader and start the measurement.</li>
	<li>Record the hourly OD600 reads and analyze as described in section 4. Compare the calculated maximal growth rate and population density of each well to determine locational bias of the 96-well reads.</li>
</ol>

<ol>
	<li>Kovarova-Kovar, K., Egli, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+kinetics+of+suspended+microbial+cells:+from+single-substrate-controlled+growth+to+mixed-substrate+kinetics.">Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics.</a> Microbiol Mol Biol Rev. 62 (3), 646-666 (1998).</li><li>Soupene, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physiological+studies+of+Escherichia+coli+strain+MG1655:+growth+defects+and+apparent+cross-regulation+of+gene+expression.">Physiological studies of Escherichia coli strain MG1655: growth defects and apparent cross-regulation of gene expression.</a> J Bacteriol. 185 (18), 5611-5626 (2003).</li><li>Sezonov, G., Joseleau-Petit, D., D'Ari, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Escherichia+coli+physiology+in+Luria-Bertani+broth.">Escherichia coli physiology in Luria-Bertani broth.</a> J Bacteriol. 189 (23), 8746-8749 (2007).</li><li>Egli, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+growth+and+physiology:+a+call+for+better+craftsmanship.">Microbial growth and physiology: a call for better craftsmanship.</a> Front Microbiol. 6, 287 (2015).</li><li>Kurokawa, M., Seno, S., Matsuda, H., Ying, B. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlation+between+genome+reduction+and+bacterial+growth.">Correlation between genome reduction and bacterial growth.</a> DNA Res. 23 (6), 517-525 (2016).</li><li>Matsumoto, Y., Murakami, Y., Tsuru, S., Ying, B. W., Yomo, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+rate-coordinated+transcriptome+reorganization+in+bacteria.">Growth rate-coordinated transcriptome reorganization in bacteria.</a> BMC Genomics. 14, 808 (2013).</li><li>Nahku, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Specific+growth+rate+dependent+transcriptome+profiling+of+Escherichia+coli+K12+MG1655+in+accelerostat+cultures.">Specific growth rate dependent transcriptome profiling of Escherichia coli K12 MG1655 in accelerostat cultures.</a> J Biotechnol. 145 (1), 60-65 (2010).</li><li>Dai, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reduction+of+translating+ribosomes+enables+Escherichia+coli+to+maintain+elongation+rates+during+slow+growth.">Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth.</a> Nat Microbiol. 2, 16231 (2016).</li><li>Madrid, R. E., Felice, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+biomass+estimation.">Microbial biomass estimation.</a> Crit Rev Biotechnol. 25 (3), 97-112 (2005).</li><li>Harris, C. M., Kell, D. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+estimation+of+microbial+biomass.">The estimation of microbial biomass.</a> Biosensors. 1 (1), 17-84 (1985).</li><li>Yates, G. T., Smotzer, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+lag+phase+and+initial+decline+of+microbial+growth+curves.">On the lag phase and initial decline of microbial growth curves.</a> J Theor Biol. 244 (3), 511-517 (2007).</li><li>Kargi, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Re-interpretation+of+the+logistic+equation+for+batch+microbial+growth+in+relation+to+Monod+kinetics.">Re-interpretation of the logistic equation for batch microbial growth in relation to Monod kinetics.</a> Lett Appl Microbiol. 48 (4), 398-401 (2009).</li><li>Peleg, M., Corradini, M. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+growth+curves:+what+the+models+tell+us+and+what+they+cannot.">Microbial growth curves: what the models tell us and what they cannot.</a> Crit Rev Food Sci Nutr. 51 (10), 917-945 (2011).</li><li>Sprouffske, K., Wagner, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growthcurver:+an+R+package+for+obtaining+interpretable+metrics+from+microbial+growth+curves.">Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves.</a> BMC Bioinformatics. 17, 172 (2016).</li><li>Hall, B. G., Acar, H., Nandipati, A., Barlow, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+rates+made+easy.">Growth rates made easy.</a> Mol Biol Evol. 31 (1), 232-238 (2014).</li><li>Hermsen, R., Okano, H., You, C., Werner, N., Hwa, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+growth-rate+composition+formula+for+the+growth+of+E.coli+on+co-utilized+carbon+substrates.">A growth-rate composition formula for the growth of E.coli on co-utilized carbon substrates.</a> Mol Syst Biol. 11 (4), 801 (2015).</li><li>Engen, S., Saether, B. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=r-+and+K-selection+in+fluctuating+populations+is+determined+by+the+evolutionary+trade-off+between+two+fitness+measures:+Growth+rate+and+lifetime+reproductive+success.">r- and K-selection in fluctuating populations is determined by the evolutionary trade-off between two fitness measures: Growth rate and lifetime reproductive success.</a> Evolution. 71 (1), 167-173 (2017).</li></ol>

We thank Kohei Tsuchiya for providing the CFU assay example. This work was partially financially supported by a Grant-in-Aid for Scientific Research (C) no. 26506003 (to BWY) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Critical steps in the protocol include the preparation of a common stock of exponentially growing cells and the replication of the same samples in multiple wells at various positions on the microplate. Previously, microbiologists started the culture from an overnight pre-culture. While this method may reduce the lag time of bacterial growth, it is difficult to achieve reproducible growth curves. As shown in Figure 2, the independent measurements using the common glycerol stocks resulted in nearly identical growth curves, which provide reproducible and precise results. As shown in Figure 3, wells at various locations give slightly different reads, which is considered the global background of the measurements. For the quantitative evaluation of bacterial growth, this background should be considered. Inoculation of samples to multiple wells at varied plate locations for experimental replication is highly recommended to account for global bias.
In addition to handling errors, the modification and troubleshooting derived from the experimental devices and supplies should be considered. Several advanced microplate readers are commercially available. They show a wide variation in characteristics, such as the method of adjusting the light wavelength, the mode of heating or cooling, and the manner and frequency of shaking. Based on our experience, evaporation should be addressed in high-throughput studies because changes in liquid volume greatly influence OD600 reads. Evaporation is caused by the method of heating used by the microplate readers and the architecture of the microplates. The two modes of heating commonly implemented in commercial microplate readers are heating by warm air and heating with a hot plate. The microplate reader used in this protocol used the hot plate as a heating method to decrease evaporation because the medium in the microplate would quickly dry up in the blowing warm air. The microplates should also be carefully selected. Different 96-well microplates (with lids) cause different levels of evaporation. The described method utilizes the plate format with a deep covered lid, which reduces evaporation.
Moreover, if the study requires stationary phase analysis of the growth curve, then the manner and frequency of shaking in the microplate reader should be taken into account. Shaking during culture ensures that the growing cells are suspended uniformly in the media. Improper shaking or low frequency may cause the cells to stack around the well edge or in the center at a high density, resulting in false reads that are either lower or higher than actual reads. The described method uses linear, bidirectional shaking. While the 8-shaped shaking mode is probably sufficient, we do not recommend the circular mode of shaking.
The major limitation of this method is the evaporation of the cell culture medium. Since the maximal culture volume is small, i.e., 200 &#181;L, incubation at 37 &#176;C causes significant evaporation. Thus, this method is unsuitable for culturing longer than 48 h. Additionally, cell cultures of low density should be avoided because optical detection (OD600) is limited at these densities. When considered together, very slow growing cells/strains or very low initial concentration cultures are not preferred, as they require longer time and are below the detection limit of OD600.
This method yields precise, high-throughput measurements, which are highly advantageous for systematic surveys. As a representative result, this method allows to evaluate an assortment of E. coli strains with various genomic sequences and/or medium easily and reproducibly5. When compared to traditional methods of culturing in tubes and manually measuring samples at different time points, the described method is less labor and time intensive. Future applications and development of the method are assumed to involve the automation of experimental manipulation and computational analysis. Automatic measurement and robotics application will lead to a highly efficient and reliable method of evaluating cell growth and performing survival tests using bacterial and mammalian cells.

Microbiological studies often start with the culture of bacterial cells and the assessment of the bacterial growth curves, which represent a fundamental phenomenon of bacterial physiology1,2,3. Basic culture principles are widely available in the published research literature and textbooks because bacterial culture is a fundamental methodology. At the bench level, substantial attention has traditionally been focused on optimizing growth media and culturing conditions, but controlling the growth rate, which would likely provide even greater understanding of microbial physiology, has not been extensively studied4. For exponentially growing bacteria, a key parameter of the cellular state is the growth rate, which has been reported to be coordinated with the genome, transcriptome, and proteome5,6,7,8. Thus, quantitative evaluation of bacterial growth is crucial for understanding microbial physiology.
To evaluate bacterial growth, the experimental methods used to estimate biomass are well established9,10 and are based on the detection of biochemical, physical, or biological parameters, such as optical turbidity. In addition, the analytical methods used to capture the dynamic properties of growth changes are commonly based on established nonlinear models11,12,13, for example, logistic equations. Growth dynamics are generally acquired by timed sampling of cell growth in culture by either measuring optical turbidity or performing colony-forming unit (CFU) assays. The limitation of these culturing and detection methods is that the data points are not a true reflection of population dynamics because the measurement intervals are often in hours and because the culture condition (e.g., changes in temperature and aeration) is disturbed at the time of sampling. Culture and analysis techniques must be updated using recent developments in technology and understanding. Recent advances in microplate readers allow the real-time observation of bacterial growth and significantly decrease labor costs. Using these advanced devices, the latest studies on bacterial growth have reported analytical methods for high-throughput measurements14,15.
The purpose of this protocol is to evaluate the precise growth dynamics in a high-throughput manner, which will be valuable for quantitative studies that ultimately address the questions of how the growth rate is determined and what factors affect the growth rate. The protocol addresses all factors that should be taken into account for the repeatable and precise quantitation of bacterial growth. The experimental method and analysis are described in detail in the main text. This method permits the precise and reproducible analysis of bacterial growth in a high-throughput manner. Microbiologists can use this protocol to derive additional quantitative results from their experimental evidence. This protocol can also be used for studies in systems biology that attempt to draw conceptual conclusions or to achieve a theoretical overview of growth.

<table>
	<tbody>
		<tr>
			<td>K2HPO4</td>
			<td>Wako</td>
			<td>164-04295</td>
			<td></td>
		</tr>
		<tr>
			<td>KH2PO4</td>
			<td>Wako</td>
			<td>166-04255</td>
			<td></td>
		</tr>
		<tr>
			<td>(NH4)2SO4</td>
			<td>Wako</td>
			<td>019-03435</td>
			<td></td>
		</tr>
		<tr>
			<td>MgSO4-7H2O</td>
			<td>Wako</td>
			<td>138-00415</td>
			<td></td>
		</tr>
		<tr>
			<td>Thiamine-HCl</td>
			<td>Wako</td>
			<td>201-00852</td>
			<td></td>
		</tr>
		<tr>
			<td>glucose</td>
			<td>Wako</td>
			<td>049-31165</td>
			<td></td>
		</tr>
		<tr>
			<td>HCl</td>
			<td>Wako</td>
			<td>080-01066</td>
			<td></td>
		</tr>
		<tr>
			<td>Iron (II) sulfate heptahydrate (FeSO4-7H2O)</td>
			<td>Wako</td>
			<td>094-01082</td>
			<td></td>
		</tr>
		<tr>
			<td>KOH</td>
			<td>Wako</td>
			<td>168-21815</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>Wako</td>
			<td>075-00611</td>
			<td></td>
		</tr>
		<tr>
			<td>Centrifuge tube (50 mL, sterilized)</td>
			<td>WATSON</td>
			<td>1342-050S</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette Tips, 200 &#181;L</td>
			<td>WATSON</td>
			<td>110-705Y</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette Tips, 1000 &#181;L</td>
			<td>WATSON</td>
			<td>110-8040</td>
			<td></td>
		</tr>
		<tr>
			<td>Microtube (1.5 mL)</td>
			<td>WATSON</td>
			<td>131-715C</td>
			<td></td>
		</tr>
		<tr>
			<td>8 multichannel-pipette</td>
			<td>WATSON</td>
			<td>NT-8200</td>
			<td></td>
		</tr>
		<tr>
			<td>PASORINA STIRRER</td>
			<td>AS ONE</td>
			<td>2-4990-02</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass cylinder (200 mL)</td>
			<td>AS ONE</td>
			<td>1-8562-07</td>
			<td></td>
		</tr>
		<tr>
			<td>Precision pH mater</td>
			<td>AS ONE</td>
			<td>AS800 / 1-054-01</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipetman P-200</td>
			<td>GILSON</td>
			<td>1-6855-05</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipetman P-1000</td>
			<td>GILSON</td>
			<td>1-6855-06</td>
			<td></td>
		</tr>
		<tr>
			<td>Disposable Serolocical Pipettes (10 mL)</td>
			<td>SANPLATEC</td>
			<td>SAN27014</td>
			<td></td>
		</tr>
		<tr>
			<td>Disposable Serolocical Pipettes (25 mL)</td>
			<td>SANPLATEC</td>
			<td>SAN27015</td>
			<td></td>
		</tr>
		<tr>
			<td>Microtube stand</td>
			<td>BM Bio</td>
			<td>801-02Y</td>
			<td></td>
		</tr>
		<tr>
			<td>Vortex</td>
			<td>BM Bio</td>
			<td>BM-V1</td>
			<td></td>
		</tr>
		<tr>
			<td>Corning Costar 96-well microplate with lid (Flat bottom, Clear)</td>
			<td>Sigma-Aldrich</td>
			<td>Corning, 3370</td>
			<td></td>
		</tr>
		<tr>
			<td>Corning Costar reagent reservoir (50 mL)</td>
			<td>Sigma-Aldrich</td>
			<td>Corning, 4870</td>
			<td></td>
		</tr>
		<tr>
			<td>Stericup GV PVDF (250 mL, 0.22 &#181;M)</td>
			<td>Merck Millipore</td>
			<td>SCGVU02RE</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipet-Aid XP</td>
			<td>DRUMMOND</td>
			<td>4-000-101</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioshaker (BR-23UM MR)</td>
			<td>TAITEC</td>
			<td>0053778-000</td>
			<td></td>
		</tr>
		<tr>
			<td>Disposal cell (1.5 mL)</td>
			<td>Kartell</td>
			<td>1938 / 2-478-02</td>
			<td></td>
		</tr>
		<tr>
			<td>DU 730 Life Science UV/Vis Spectrophotometer</td>
			<td>Beckman Coulter</td>
			<td>A23616</td>
			<td></td>
		</tr>
		<tr>
			<td>EPOCH2</td>
			<td>BioTek</td>
			<td>2014-EP2-002 / EPOCH2T</td>
			<td></td>
		</tr>
		<tr>
			<td>Beaker (500 mL)</td>
			<td>IWAKI</td>
			<td>82-0008</td>
			<td></td>
		</tr>
		<tr>
			<td>BIO clean bench</td>
			<td>Panasonic</td>
			<td>MCV-B131F</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass tubes</td>
			<td>NICHIDEN RIKA GLASS</td>
			<td>P-10M~P-30 /101019</td>
			<td></td>
		</tr>
		<tr>
			<td>Silicone rubber stoppers</td>
			<td>ShinEtsu Polymer</td>
			<td>T-19</td>
			<td></td>
		</tr>
		<tr>
			<td>Bacterial strains</td>
			<td>Strain bank organization; National Bio Resource Project (NBRP) in Japan</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

precise, high-throughput analysis of bacterial growth

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the systems level. Recent studies have reported correlations between bacterial growth and genome-wide events, such as genome reduction and transcriptome reorganization. Correctly analyzing bacterial growth is crucial for understanding the growth-dependent coordination of gene functions and cellular components. Accordingly, the precise quantitative evaluation of bacterial growth in a high-throughput manner is required. Emerging technological developments offer new experimental tools that allow updates of the methods used for studying bacterial growth. The protocol introduced here employs a microplate reader with a highly optimized experimental procedure for the reproducible and precise evaluation of bacterial growth. This protocol was used to evaluate the growth of several previously described Escherichia coli strains. The main steps of the protocol are as follows: the preparation of a large number of cell stocks in small vials for repeated tests with reproducible results, the use of 96-well plates for high-throughput growth evaluation, and the manual calculation of two major parameters (i.e., maximal growth rate and population density) representing the growth dynamics. In comparison to the traditional colony-forming unit (CFU) assay, which counts the cells that are cultured in glass tubes over time on agar plates, the present method is more efficient and provides more detailed temporal records of growth changes, but has a stricter detection limit at low population densities. In summary, the described method is advantageous for the precise and reproducible high-throughput analysis of bacterial growth, which can be used to draw conceptual conclusions or to make theoretical observations.

The described method provides a means to capture dynamic bacterial growth in a continuous, high-throughput manner by utilizing a 96-well format reader that takes multiple optical density measurements at various time intervals (from minutes to hours to days). The growth curves of an assortment of E. coli strains expressing various genomes can be precisely acquired in a single experiment (Figure 1A). In comparison to the described method, the traditional method (the CFU assay) generally requires longer sampling time intervals (Figure 1B) and is labor intensive if multiple culture is required. In addition, each culture sampling time for the CFU assay requires the use of a small volume of cell culture that cannot be used for repeated measurements. Additionally, limited numbers of time points for detection may miss the sampling points that are needed for the correct calculation of the growth rate and the maximal OD600. However, the CFU assay has a lower detection limit, 103 - 104 cells/mL, making it at least two orders of magnitude more sensitive than the optical turbidity assay (OD600= 0.001, which has a detection limit of approximately 105 - 106 cells/mL). The described method provides a practical and efficient experimental tool for systems-level studies on growth dynamics.
A key advantage of this protocol is that it takes repeated measurements from a common glycerol stock. Having multiple cell cultures at various dilution rates in early exponential growth phase is highly useful because it allows researchers to determine the culture time at any timescale and avoids having saturated cultures due to unpredictable events. Repeated culturing and measurements of the common stocks, which were prepared from one of the five cultures initiated using different dilution rates as described in Protocol section 2.2 (Figure 2A), presented highly similar outcomes of the growth dynamics analysis. Multiple growth curves of repeated and independent measurements (N= 6) overlapped well (Figure 2B, top panel), with little variance (Figure 2B, bottom panel), particularly during the exponential phase (Figure 2B, shadowed area). These results suggest that the described method delivers highly reproducible results.
Estimating the global bias of the 96-well reads is highly recommended. In the present method, the global bias is estimated by monitoring the growth of the wild-type E. coli strain W3110 as described in Protocol section 5. For example, the growth rates calculated from the growth curves showed significant variation among the 96 wells (Figure 3A), representing the global fluctuation derived from both the data manipulation and the device errors. These errors can be masked by loading the same sample into multiple wells at varied locations on the plate. By contrast, the maximal OD600 showed a clear location-dependent bias for the results, as gradual changes were observed along the vertical direction, that is, the maximal OD600 gradually increased from the wells of column 2 to those of column 11 on the 96-well plate (Figure 3B). To avoid the biased results, the same sample is recommended to be loaded into the wells located on the both sides of the 96-well plate for repeated measurements. The locational bias in maximal OD600 values is assumed to result from the variation in evaporation rates, which are determined by both heating and sealing efficiencies. Compared with the wells in the middle of the 96-well microplate, the wells located at the edges tended to show relatively higher values, reflecting the varied evaporation rates resulting from the sealing effect of the microplate. In addition, since other 96-well microplates subjected to the same bias test showed similar directional changes based on where the well was located on the plate, the extent of the bias might depend on the plate reader. Thus, it is important to evaluate the global bias of the 96-well reads in studies that determine maximal population density. Commercially available plate readers all have their specific global bias caused by the heating and shaking efficiencies, and the 96-well microplates are largely differentiated by sealing efficiency.
This method is designed for the systematic analysis of bacterial growth, and it can be used to analyze two major parameters, i.e., growth rate and cell density, that are commonly applied in a wide variety of fields, such as systems biology, evolution, and ecology16,17. The manual calculation using spreadsheet is introduced to show the processing of the raw reads to produce the evaluated parameters of growth rate and cell density. The hourly growth rates are first calculated from the raw reads of the growth curve (Figure 4A) according to Protocol section 4.6 as a data set of sequential changes in growth rates (Figure 4B). The means and standard deviations of every five consecutive growth rates are acquired sequentially (Figure 4C-D), and the largest mean growth rate with the smallest standard deviation is defined as the maximal growth rate of the growth curve (Figure 4C-D, highlighted). The maximal cell density (OD600) is analyzed similarly (Figure 4E-F). The calculated growth rate and cell density are subsequently subjected to further analysis. Note that the described calculations can be automatically performed using computational platforms (programming languages), e.g., R and Python.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56197/56197fig1.jpg" /> 
Figure 1 Growth Curves Acquired with Diverse Methods. (A) Multiple acquisitions of growth curves. Growth curves of four different E. coli strains expressing various genomes were obtained in a high-throughput manner. The E. coli cells were grown in minimal medium M63, and the changes in OD600 reads were recorded by a microplate reader at 1-h intervals. The strains from left to right are the wild type E. coli W3110 (Number 0) and its reduced genomes numbers 1, 10 and 18 (described previously5), respectively. (B) Growth curve obtained by using the CFU assay. The wild-type E. coli cells (Number 0) were cultured and were sampled at the indicated time points over several hours. The growth changes were estimated using the CFU assay. Open circles indicate the recording or sampling points. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56197/56197fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56197/56197fig2.jpg" /> 
Figure 2. Reproducible Repeated Measurements. (A) Overnight cultures of varied dilution rates. The E. coli cells were inoculated in tubes at multiple dilution rates, which varied from 1- to 10,000-fold, as indicated. The cells were cultured at 37 &#176;C with shaking at 200 rpm for approximately 12 h. The final OD600 values of the five cell cultures (from left to right) were 1.51, 0.74, 0.05, 0.008 and 0.001, respectively. The culture with OD600 of 0.05 was used for the common glycerol stock. (B) Highly reproducible measurements. Six independent growth curves (top, gray lines) of the same E. coli strain were acquired and calculated from six positions on the microplate and two vials of the common glycerol stock. The black line represents the average of the six growth curves. The coefficients of variation (CV) of the six repeated measurements were calculated (bottom), and the steady low points are shadowed in pink. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56197/56197fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56197/56197fig3.jpg" /> 
Figure 3 Global Bias of the 96-well Format. (A) Heat map of growth rates. The E. coli cell stock was suspended in minimal medium M63 and loaded equally in the 96 wells of the microplate (represented as a 12 &#215; 8 table). The changes in OD600 were obtained using the microplate reader, as described in section 5 of the protocol. The gradation from white to red indicates the growth rates from 0.7 to 0.9 h-1. (B) Heat map of the maximal OD600. The gradation from white to red indicates the maximal OD600 values from 0.9 to 1.2. The calculations for the growth rate and maximal OD600 are as described in Figure 4. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56197/56197fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56197/56197fig4.jpg" /> 
Figure 4 Analysis of the Growth Curves. (A) Raw data of growth curves. The hourly OD600 reads are plotted against the culture time. Open circles indicate the sampling times. (B) Temporal changes in growth rates. Hourly growth rates between two consecutive OD600 reads are calculated according to the equation described in 4.6. (C) Mean growth rates. The mean of five consecutive growth rates are calculated to give smooth growth rates. (D) Standard deviations of mean growth rates. The standard deviation of the same five consecutive growth rates is calculated. The maximal growth rate with the smallest standard deviation is highlighted in red. (E) Mean OD600. The mean of five consecutive OD600 values is calculated to determine the smooth OD600. (F) Standard deviations of mean OD600 values. Standard deviation of the same five consecutive OD600 values is calculated. The maximal OD600 with the smallest standard deviation is highlighted in red. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56197/56197fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Precise, High-throughput Analysis of Bacterial Growth at JoVE.com

Precise, High-throughput Analysis of Bacterial Growth

Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the ...

precise-high-throughput-analysis-of-bacterial-growth

Nanyang Technological University

Research

JoVE Journal

Biology

23.7K Views.  University of Tsukuba. Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

Video: Precise, High-throughput Analysis of Bacterial Growth

High-throughput Yeast Plasmid Overexpression Screen

The budding yeast, Saccharomyces cerevisiae, is a powerful model system for defining fundamental mechanisms of many important cellular processes, including those with direct relevance to human disease. Because of its short generation time and well-characterized genome, a major experimental advantage of the yeast model system is the ability to perform genetic screens to identify genes and pathways that are involved in a given process. Over the last thirty years such genetic screens have been used to elucidate the cell cycle, secretory pathway, and many more highly conserved aspects of eukaryotic cell biology 1-5. In the last few years, several genomewide libraries of yeast strains and plasmids have been generated 6-10. These collections now allow for the systematic interrogation of gene function using gain- and loss-of-function approaches 11-16. Here we provide a detailed protocol for the use of a high-throughput yeast transformation protocol with a liquid handling robot to perform a plasmid overexpression screen, using an arrayed library of 5,500 yeast plasmids. We have been using these screens to identify genetic modifiers of toxicity associated with the accumulation of aggregation-prone human neurodegenerative disease proteins. The methods presented here are readily adaptable to the study of other cellular phenotypes of interest.

Here we describe a plasmid overexpression screen in Saccharomyces cerevisiae, using an arrayed plasmid library and a high-throughput yeast transformation protocol with a liquid handling robot.

The budding yeast, Saccharomyces cerevisiae, is a powerful model system for defining fundamental mechanisms of many important cellular processes, ...

High-throughput Purification of Affinity-tagged Recombinant Proteins

X-ray crystallography is the method of choice for obtaining a detailed view of the structure of proteins. Such studies need to be complemented by further biochemical analyses to obtain detailed insights into structure/function relationships. Advances in oligonucleotide- and gene synthesis technology make large-scale mutagenesis strategies increasingly feasible, including the substitution of target residues by all 19 other amino acids. Gain- or loss-of-function phenotypes then allow systematic conclusions to be drawn, such as the contribution of particular residues to catalytic activity, protein stability and/or protein-protein interaction specificity.
In order to attribute the different phenotypes to the nature of the mutation - rather than to fluctuating experimental conditions - it is vital to purify and analyse the proteins in a controlled and reproducible manner. High-throughput strategies and the automation of manual protocols on robotic liquid-handling platforms have created opportunities to perform such complex molecular biological procedures with little human intervention and minimal error rates1-5.
Here, we present a general method for the purification of His-tagged recombinant proteins in a high-throughput manner. In a recent study, we applied this method to a detailed structure-function investigation of TFIIB, a component of the basal transcription machinery. TFIIB is indispensable for promoter-directed transcription in vitro and is essential for the recruitment of RNA polymerase into a preinitiation complex6-8. TFIIB contains a flexible linker domain that penetrates the active site cleft of RNA polymerase9-11. This linker domain confers two biochemically quantifiable activities on TFIIB, namely (i) the stimulation of the catalytic activity during the 'abortive' stage of transcript initiation, and (ii) an additional contribution to the specific recruitment of RNA polymerase into the preinitiation complex4,5,12 . We exploited the high-throughput purification method to generate single, double and triple substitution and deletions mutations within the TFIIB linker and to subsequently analyse them in functional assays for their stimulation effect on the catalytic activity of RNA polymerase4. Altogether, we generated, purified and analysed 381 mutants - a task which would have been time-consuming and laborious to perform manually. We produced and assayed the proteins in multiplicates which allowed us to appreciate any experimental variations and gave us a clear idea of the reproducibility of our results.
This method serves as a generic protocol for the purification of His-tagged proteins and has been successfully used to purify other recombinant proteins. It is currently optimised for the purification of 24 proteins but can be adapted to purify up to 96 proteins.

We describe a method for the affinity-tagged purification of recombinant proteins using liquid-handling robotics. This method is generally applicable to the small-scale purification of soluble His-tagged proteins in a high-throughput format.

X-ray crystallography is the method of choice for obtaining a detailed view of the structure of proteins. Such studies need to be complemented by further ...

High Throughput Microinjections of Sea Urchin Zygotes

Microinjection into cells and embryos is a common technique that is used to study a wide range of biological processes. In this method a small amount of treatment solution is loaded into a microinjection needle that is used to physically inject individual immobilized cells or embryos. Despite the need for initial training to perform this procedure for high-throughput delivery, microinjection offers maximum efficiency and reproducible delivery of a wide variety of treatment solutions (including complex mixtures of samples) into cells, eggs or embryos. Applications to microinjections include delivery of DNA constructs, mRNAs, recombinant proteins, gain of function, and loss of function reagents. Fluorescent or colorimetric dye is added to the injected solution to enable instant visualization of efficient delivery as well as a tool for reliable normalization of the amount of the delivered solution. The described method enables microinjection of 100-400 sea urchin zygotes within 10-15 min.

Microinjection is a common technique used to deliver DNA constructs, mRNAs, morpholino antisense oligonucleotides or other treatments into eggs, embryos, and cells of various species.

Microinjection into cells and embryos is a common technique that is used to study a wide range of biological processes. In this method a small amount of ...

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design1,2. Here, a peptide-MHC II binding assay is scaled to 384-well format. The scaled down protocol reduces reagent costs by 75% and is higher throughput than previously described 96-well protocols1,3-5. Specifically, the experimental design permits robust and reproducible analysis of up to 15 peptides against one MHC II allele per 384-well ELISA plate. Using a single liquid handling robot, this method allows one researcher to analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. Others working in the fields of protein deimmunization or vaccine design and development may find the protocol to be useful in facilitating their own work. In particular, the step-by-step instructions and the visual format of JoVE should allow other users to quickly and easily establish this methodology in their own labs.

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design. &#160;Here, a peptide-MHC II binding assay scaled to 384-well plates is described. This cost effective format should prove useful in the fields of protein deimmunization and vaccine design and development.

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or ...

Recovery of Adult Zebrafish Hearts for High-throughput Applications

Use of the zebrafish model system for studying development, regeneration, and disease is expanding toward use of adult hearts for cell dissociation and purification of RNA, DNA, and proteins. All of these applications demand the rapid recovery of significant numbers of zebrafish hearts to avoid gene regulatory, metabolic, and other changes that begin after death. Adult zebrafish hearts are also required for studying heart structure for a variety of mutants and for studying heart regeneration. However, the traditional zebrafish heart dissection is slow and difficult and requires specialized tools, making large-scale dissection of adult zebrafish hearts tedious. Traditional methods also harbor the risk of damaging the heart during the dissection. Here, we describe a method for dissection of adult zebrafish hearts that is fast, reproducible, and preserves heart architecture. Furthermore, this method does not require specialized tools, is painless for the zebrafish, can be performed on fresh or fixed specimens, and can be performed on zebrafish as young as one month old. The approach described expands the use of adult zebrafish for cardiovascular research.

Use of zebrafish for cardiovascular research is expanding towards research on adult hearts. For these applications, quick and simple isolation of cardiac tissues is key to avoid post-mortem changes and to obtain an adequate number of samples. Here, we describe a fast and reproducible method for dissecting adult zebrafish hearts.

Use of the zebrafish model system for studying development, regeneration, and disease is expanding toward use of adult hearts for cell dissociation and ...

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic chromosome, synIII1, and one synthetic chromosome arm, synIXR2, have been constructed and their in vivo function validated in the absence of the corresponding wild type chromosomes. An important design feature of Sc2.0 chromosomes is the introduction of PCRTags, which are short, re-coded sequences within open reading frames (ORFs) that enable differentiation of synthetic chromosomes from their wild type counterparts. PCRTag primers anneal selectively to either synthetic or wild type chromosomes and the presence/absence of each type of DNA can be tested using a simple PCR assay. The standard readout of the PCRTag assay is to assess presence/absence of amplicons by agarose gel electrophoresis. However, with an average PCRTag amplicon density of one per 1.5 kb and a genome size of ~12 Mb, the completed Sc2.0 genome will encode roughly 8,000 PCRTags. To improve throughput, we have developed a real time PCR-based detection assay for PCRTag genotyping that we call qPCRTag analysis. The workflow specifies 500 nl reactions in a 1,536 multiwell plate, allowing us to test up to 768 PCRTags with both synthetic and wild type primer pairs in a single experiment.

Designer chromosomes of the Synthetic Yeast Genome project, Sc2.0, can be distinguished from their native counterparts using a PCR-based genotyping assay called PCRTagging, which has a presence/absence endpoint. Here we describe a high-throughput real time PCR detection method for PCRTag genotyping.

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic ...

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis

Many microorganisms are capable of producing and secreting exopolysaccharides (EPS), which have important implications in medical fields, food applications or in the replacement of petro-based chemicals. We describe an analytical platform to be automated on a liquid handling system that allows the fast and reliable analysis of the type and the amount of EPS produced by microorganisms. It enables the user to identify novel natural microbial exopolysaccharide producers and to analyze the carbohydrate fingerprint of the corresponding polymers within one day in high-throughput (HT). Using this platform, strain collections as well as libraries of strain variants that might be obtained in engineering approaches can be screened. The platform has a modular setup, which allows a separation of the protocol into two major parts. First, there is an automated screening system, which combines different polysaccharide detection modules: a semi-quantitative analysis of viscosity formation via a centrifugation step, an analysis of polymer formation via alcohol precipitation and the determination of the total carbohydrate content via a phenol-sulfuric-acid transformation. Here, it is possible to screen up to 384 strains per run. The second part provides a detailed monosaccharide analysis for all the selected EPS producers identified in the first part by combining two essential modules: the analysis of the complete monomer composition via ultra-high performance liquid chromatography coupled with ultra violet and electrospray ionization ion trap detection (UHPLC-UV-ESI-MS) and the determination of pyruvate as a polymer substituent (presence of pyruvate ketal) via enzymatic oxidation that is coupled to a color formation. All the analytical modules of this screening platform can be combined in different ways and adjusted to individual requirements. Additionally, they can all be handled manually or performed with a liquid handling system. Thereby, the screening platform enables a huge flexibility in order to identify various EPS.

We present an automated modular high-throughput-method for the identification and characterization of microbial exopolysaccharides in small scale. This method combines a fast preselection to analyze the total amount of secreted polysaccharides with a detailed carbohydrate fingerprint to enable the fast screening of newly isolated bacterial strains or entire strain collections.

Many microorganisms are capable of producing and secreting exopolysaccharides (EPS), which have important implications in medical fields, food ...

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of energy homeostasis and obesity. The small size of zebrafish allows for assays on embryos to be conducted in a 96- or 384-well plate format, Morpholino and CRISPR based technologies promote ease of genetic manipulation, and drug treatment by bath application is viable. Moreover, zebrafish are ideal for forward genetic screens allowing for novel gene discovery. Given the relative novelty of zebrafish as a model for obesity, it is necessary to develop tools that fully exploit these benefits. Herein, we describe a method to measure energy expenditure in thousands of embryonic zebrafish simultaneously. We have developed a whole animal microplate platform in which we use 96-well plates to isolate individual fish and we assess cumulative NADH2 production using the commercially available cell culture viability reagent alamarBlue. In poikilotherms the relationship between NADH2 production and energy expenditure is tightly linked. This energy expenditure assay creates the potential to rapidly screen pharmacological or genetic manipulations that directly alter energy expenditure or alter the response to an applied drug (e.g. insulin sensitizers).

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism for the study of energy homeostasis. By utilizing a NADH2 sensitive redox indicator, alamar Blue, we have developed an assay that measures the metabolic rate of zebrafish larvae in a 96 well plate format and can be applied to drug or gene discovery.

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of ...

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious projects to disrupt every gene in the mouse genome. The research community needs rapid and inexpensive means to phenotype tissues via histology. Histological analyses of skeletal tissues are often time consuming and semi-quantitative at best, regularly requiring subjective interpretation of slides from trained individuals. Here, we present a cryohistological paradigm for efficient and inexpensive phenotyping of mineralized tissues. First, we present a novel method of tape-stabilized cryosectioning that preserves the morphology of mineralized tissues. These sections are then adhered rigidly to glass slides and imaged repeatedly over several rounds of staining. The resultant images are then aligned either manually or via computer software to yield composite stacks of several layered images. The protocol allows for co-localization of numerous molecular signals to specific cells within a given section. In addition, these fluorescent signals can be quantified objectively via computer software. This protocol overcomes many of the shortcomings associated with histology of mineralized tissues and can serve as a platform for high-throughput, high-content phenotyping of musculoskeletal tissues moving forward.

In this manuscript, we present a high-throughput, semi-automated cryohistology platform to produce aligned composite images of multiple response measures from several rounds of fluorescent imaging on frozen sections of mineralized tissues.

There is an increasing need for efficient phenotyping and histopathology of a variety of tissues. This phenotyping need is evident with the ambitious ...

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